Image: A hillside a bloom - a place where I run my ecohydrologic model RHESSYs
Environmental Modelling: An overview.
Computer-based modeling and simulation are widely used tools in both practical environmental problem solving and in environmental research. Models give us a way to look at the world through a mixture of data and theory. A good model can help us to understand how the world works and how decisions that we make might change the world in ways that are important to us. There are many different types of models, from simple to complex, and models are often tailored to answer a specific questions. This course will give you skills that help you to choose which model, or modeling technique, is right for you - given the task at hand. The course will cover designing a new model and evaluating existing models. We will emphasize best practices, such as sensitivity and uncertainty analysis, that help to design and use models to reliably support environmental problem solving. This is a skills based course and we will use R (a data analysis and programming environment) as our basic platform.
Class will include a mix of lectures and in class hands-on examples, using students’ own computers. I will often provide an R-markdown document for you to go through prior to class so you can learn at your own pace and we will then use class time for the hands-on examples and assignments.
Instructor: Naomi Tague (https://tagueteamlab.org/)
Teaching assistant: Rachel Torres
Gain familiarity with different types of models and the situations where you might use them
Understand how to choose the ‘right model’ for the job
Know how to build simple models including
Gain some basic skills that are useful in applying models including
I will assume that everyone has some basic R skills (from ESM 203, ESM 232, MEDS program courses or other courses), including how to use ggplot, and Rmarkdown and build simple functions, as well as a basic understanding if git and Github
Many classes will be working classes so bring these to class
| Week | Lecture topics |
|---|---|
| April 17 | Into and Conceptual Models |
| April 24 | Constructing Simple Models in R |
| May 1 | Sensitivity Analysis |
| May 8 | Choosing and Evaluating Models |
| Special Class | Model Calibration |
| May 15 | Dynamic Models |
| May 22 | Stability and Sensitivity with Dynamic Models |
| May 29 | Matrix Population (Discrete Dynamic) |
| June 4 | Optimization and Wrap Up |
There are 8 assignments. Some assignments will be done in groups. Assignments will vary in length but most will be short coding assignments with a 1- paragraph write up. Assignments can be submitted as a link to your GitHub repository that has assignment files - the link will be submitted on Canvas - that way we can keep track of grading for each assignment. If you find GitHub too challenging then you can also submit files directly (if you do this, please make sure you zip multiple files together)
Learning to program is hard and I may not always explain in a way that is accessible to you - So if you don’t understand something ASK
Environmental modeling and the coding involved gets better with practice and play - Don’t just read the Rmarkdown - try the code, try variations on the ideas presented, make up stuff to try, get your feet wet
Programming means making mistakes, expect it, stay calm and try again - if you get frustrated step away and come back; be creative
Respect and Support each other
If you are really struggling, reach out to Rachel or myself, we can help (or if you just want to chat about something )